However, large scale networks such as social networks and human brain tissue networks, in the real world, exist with multiple layers of embedded or independent networks. For example, in case of social networks, a user is usually present in multiple social media platforms like Twitter, Facebook, Youtube, etc. and exhibits different level of interaction and behavior in each network as given in Figure REF . Although, few existing models focus on representing nodes based on multiplex networks [1]}, [2]}, these representations can be made more robust by exploring random walk techniques that are dedicated for multiplex networks. Using these techniques, an embedding technique that accounts for the multi-layered existence and interaction of the nodes will equip learning algorithms with a richer vocabulary for learning tasks like link prediction across different networks. With this multiplex node embeddings, we explore a network reconstruction task. For example, by learning a node's interaction patterns in the retweet and mentions network, can we predict the underlying friend-follower network structure? This question is one of the motivating examples for our study and presents an interesting use-case of Multi-Net.

